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Validation of Genes Sensitive to P23 Levels in a Different MCF-7 Cell Line Overexpressing P23 [Clone #8]

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Supplementary Figure 1 – Validation of genes sensitive to p23 levels in a different MCF-7 cell line overexpressing p23 [clone #8]. qRT-PCR experiments were performed to show the relative fold-change (RFC) in the expression of six genes from Figure 1c between MCF-7 control (white bars) and MCF-7+p23 (#8) cells (black bars). Data are means of three independent experiments, normalized to GAPDH, and presented as RFC from that of MCF-7 control cells, set to 1. Error bars represent standard deviation. Supplementary Figure 2 – Genes most upregulated or downregulated by p23 overexpression are also overexpressed or under- expressed in invasive breast cancers Differential analyses of microarray datasets from invasive breast cancer subgroups (listed in the Legend) were performed using the Oncomine database (www.oncomine.org) to determine if the ten genes most upregulated or the eighteen genes most downregulated ( > 5-fold, Tables S5-6) by p23 overexpression are also over- or under-expressed in invasive breast cancers. Shown are five of the 10 upregulated and 11 of the 18 downregulated genes that are significantly over- or under-expressed in at least one invasive breast cancer dataset (p-value < 0.05) (* designates those genes that are not considered significant using Oncomine differential analysis, but are associated with invasive cancers in referenced literature). Over-expressed genes ranked highest, based on significance, within a particular dataset are designated with red boxes (or dark blue boxes for under-expressed genes). Pink and light blue boxes indicate genes that are statistically significant, but with lower gene rank. White boxes designate genes that are not statistically significant within a given dataset. Grey boxes indicate that gene expression was not measured for a particular dataset. Supplementary Figure 3 – p23 mediated changes in gene expression upon inhibition of Hsp90 A) MCF-7+p23 cells were treated with either DMSO vehicle or 1μM GA for two hours prior to protein extraction. p23-Hsp90 co- immunoprecipitation experiments were performed following cell lysis [50mM Hepes, pH 7.6, 150mM NaCl, 1mM EDTA, 1mM EGTA, 1mM NaF, 1% Triton X-100, and 10% glycerol, 3mM DTT, 1mM PMSF, 5mM ATP, 20mM sodium molybdinate and protease inhibitor cocktail (Sigma)] and pre- cleared with protein-G agarose beads (Roche). Protein lysate (8mg) was incubated overnight at 4°C with 10μg HA.11 antibody (Covance) and 0.2% input and 1/5 of the IP reaction was immunoblotted with anti-Hsp90 (1:1000; Transduction Laboratories, H38220) or p23. B) MCF-7+p23 cells were hormone-starved for three days and treated for 48 hours with GA (left panel) or 24 hours with 17-AAG (right panel). RNA was extracted and qRT-PCR performed. Data are means, normalized to 28S (left panel) or GAPDH (right panel), and expressed as relative fold-change (RFC) in gene expression to vehicle treated MCF-7+p23 cells, set to 1. Error bars represent standard deviation. Supplementary Figure 4 – p23 levels can influence the estrogen sensitivity of some, but not all genes Venn diagrams show the distribution of estrogen sensitivity of 357 genes identified in control and MCF-7+p23 cells. 116 were sensitive to E2 in both cell types (green overlap; 47 induced, 69 repressed). There were 51 induced and 47 repressed genes in control cells (yellow circles) that were not induced in MCF-7+p23 cells, suggesting these genes were made unresponsive to E2 by p23 overexpression. Conversely, 65 genes were induced and 78 repressed by E2 only in MCF-7+p23 cells, suggesting that p23 overexpression makes these genes more responsive to E2 (blue circles). When examined closely, the majority of the 65 genes that were uniquely induced or downregulated by E2 in the MCF-7+p23 cells displayed between a 1.2 and 1.9-fold change in mRNA expression by E2 in control cells and greater than 2-fold change in MCF-7+p23 cells (data not shown). There were also a small number of genes (11) not regulated by E2 in control cells (fold change < 1.2), but induced (or repressed) 2-fold or more by E2 upon p23 overexpression. Therefore, these genes can be classified as being highly reactive to E2 by p23 overexpression and are listed in Supplementary Table 7. Supplementary Figure 5 – Semi-quantitative analysis of ER recruitment to PMP22 and ABCC3 EREs A schematic representation of the PMP22 and ABCC3 loci. Arrows represent transcriptional start sites (TSS), white boxes represent exons, the stripped boxes represent the ER binding elements (ERE) as determined by global ChIP-Chip analysis and black boxes represent the control upstream sequences (UPS). ChIP assays were preformed in MCF-7 control and MCF-7+p23 cells in the absence (ethanol vehicle treated) and presence of E2 using an antibody against ER or equivalent amount of non-specific rabbit IgG (Sigma). Relative levels of ER binding to 0.1% and 0.05% of the total input are shown by PCR and are representative of at least three independent experiments. Supplementary Figure 6 - p23 expression in human breast cancer A breast cancer tissue microarray containing 213 specimens was stained by immunohistochemistry with anti-p23 antibody and the intensity of p23 staining in the nucleus and cytoplasm was scored on a scale of 0 to 3+, with 3+ being the highest intensity staining. Example specimens scored as 2+ (left panel) and 3+ (right panel) for cytoplasmic p23 from biopsies are shown. Insets magnify a portion of each image. Supplementary Figure 7 – Subcellular distribution of p23 in MCF-7 control and MCF- 7+p23 cells as a function of ER agonist and antagonist treatment Nuclear and cytoplasmic fractions were prepared using Pierce Biotechnology’s NE-PER Nuclear and Cytoplasmic Extraction Reagents (78833, Pierce Biotechnology) according to manufacturer’s instructions, from 16h ethanol vehicle (-), 1nM 17--estradiol (E), 100nM 4-OH-tamoxifen (T), or 100nM ICI 182,780 (I) treated MCF-7 control and MCF-7+p23 cells. Levels of p23, TFII-I, and -tubulin were determined by immunoblotting using a TFII-I antibody (1:1000; Cell Signaling, 4562) or antibodies to p23 and tubulin as described previously in the Materials and Methods. Shown is a representative experiment that was repeated three times with similar results. Supplementary Figure 8 – Enhanced activation of AKT and increased cytoplasmic phosphoproteome in MCF-7+p23 cells A) The cytoplasmic and nucleoplasmic fractions from MCF-7 control and MCF-7+p23 cells were prepared as described in Narita et al. (Cell 2006 126:503-14) from cells pretreated for 30 min at 37°C with the phosphatase inhibitor Calyculin A (100ng/ml; Cell Signaling, 9902) to stabilize the phosphoproteome. Fractions were immunoblotted for phospho-AKT S473 (1:1000; Cell Signaling, 9271), total AKT (1:1000; Cell Signaling, 9272), and tubulin as described in the Materials and Methods section. B) Cytoplasmic and nucleoplasmic fractions from MCF-7 control and MCF-7+p23 cells were also probed for phosphoproteins using phospho-AKT substrate antibody (1:1000, Cell Signaling, 9614). Arrowheads (black and white) indicate phosphoproteins in greater abundance in the p23 overexpressing cells relative to controls. The immunoreactivity was visualized using the Odyssey Infrared Imaging System. Figure S1 MCF-7 MCF-7+p23 (clone #8) 140 25 10 120 20 8 100 80 15 6 RFC 60 10 4 40 5 2 20 0 0 0 PMP22 ABCC3 AGR2 1.4 1.4 1.2 1.2 1.2 1 1 1 0.8 0.8 0.8 0.6 RFC 0.6 0.6 0.4 0.4 0.4 0.2 0.2 0.2 0 0 0 P8 TM4SF1 Sox3 Figure S2 * * * Figure S3 A input IP:HA (p23) 1μM GA - + - + Hsp90 WB:Hsp90 HA-p23 WB:p23 p23 B 2 2 MCF-7+p23_DMSO 4 MCF-7+p23_DMSO 25 MCF-7+p23_GA MCF-7+p23_17-AAG 20 1.5 3 1.5 15 1 2 1 RFC RFC 10 0.5 1 0.5 5 0 0 0 0 pS2 cmyc PMP22 ABCC3 AGR2 pS2 cmyc PMP22 ABCC3 AGR2 Figure S4 E2 Induced Genes E2 Repressed Genes MCF7-WT MCF7-p23 MCF7-WT MCF7-p23 51 47 65 47 69 78 98 112 # of genes 116 147 differentially regulated by E2 Figure S5 PMP22 ABCC3 ERE UPS P2 UPS P ERE -395kb -6kb +1bp -3 kb +1bp +16kb MCF-7 MCF-7 + p23 10nM E2 - + - + input input input input IgG ER IgG ER IgG ER IgG ER PMP22 ERE PMP22 UPS ABCC3 ERE ABCC3 UPS 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Figure S6 cytoplasmic p23 2+ cytoplasmic p23 3+ Figure S7 cytoplasmic nuclear MCF-7 MCF-7+p23 MCF-7 MCF-7+p23 E T I E T I E T I E T I HA-p23 p23 TFII-I Tubulin Figure S8 A B cytoplasmic nuclear MW MCF-7+p23 MCF-7 MCF-7 MCF-7+p23 170 130 MCF-7 MCF-7+p23 100 P-AKT 70 55 AKT 40 35 Tubulin 25 15 10 Phospho Akt Substrate Antibody Table S1 qRT-PCR Gene Expression Primers GAPDH_F 5' - CCTCAACGACCACTTTGTCA - 3' GAPDH_R 5' - CCCTGTTGCTGTAGCCAAAT -3' 28S_F 5' - AAACTCTGGTGGAGGTCCGT -3' 28S_R 5' - CTTACCAAAAGTGGCCCACTA -3' pS2_F 5' - GAACAAGGTGATCTGCG -3' pS2_R 5' - TGGTATTAGGATAGAAGCACCA -3' PMP22_F 5' - GTGCTGCTGTTCGTCTCCAC -3' PMP22_R 5' - ATCAGTTGCGTGTCCATTGC -3' ABCC3_F 5' - TTGTCGTGGCTACATCATCC -3' ABCC3_R 5' - AAAAGGTCCGCCCAGGAG -3' AGR2_F 5' - ACTTGGATGAGTGCCCACACAG -3' AGR2_R 5' - CAGATTGAGGAGGACAAACTGCTC -3' Sox3_F 5’ - ACTCATCAGGTGCGAGAAGC – 3’ Sox3_R 5’ - GGGAAGGGTAGGCTTATCAA – 3’ p8_F 5’ - GAGGAAACTGGTGACCAAGC – 3’ p8_R 5’ - CCGTCTCTATTGCTGGGTGT -3’ TM4SF1_F 5’ - TTTGCTGCTCTCACCAACAG – 3’ TM4SF1_R 5’ - GTAGACTGTGGGGAGTATGTTACAC – 3’ Table S2 ChIP primers PMP22(ERE)F 5'- GCCGCATAAAATGTCACGCC -3' PMP22(ERE)R 5'- AAACCTACTTGGGGGAGTCTGC -3' PMP22(UPS)F 5'- TCCGTGGAACCTAAGTCTTGGAG-3' PMP22(UPS)R 5'- GCAACTGTGTCTTGGGAATCGC - 3' PMP22(P2)F 5'- GAGCCAGTTTCTCGGTCAAC -3' PMP22(P2)R 5'- GCGGCGACTTTTACTCGAA -3' ABCC3(ERE)F 5'- GCTGCCACAGCACTAAACTGT -3' ABCC3(ERE)R 5'- GAGTGAGGTCCGGGTTTTCT -3' ABCC3(UPS)F 5'- GCAAGGGGCTAAAAGGAATC -3' ABCC3(UPS)R 5'- AATGCTCCGAAGGGAGAGAG
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    Supplementary Materials COMPARATIVE ANALYSIS OF THE TRANSCRIPTOME, PROTEOME AND miRNA PROFILE OF KUPFFER CELLS AND MONOCYTES Andrey Elchaninov1,3*, Anastasiya Lokhonina1,3, Maria Nikitina2, Polina Vishnyakova1,3, Andrey Makarov1, Irina Arutyunyan1, Anastasiya Poltavets1, Evgeniya Kananykhina2, Sergey Kovalchuk4, Evgeny Karpulevich5,6, Galina Bolshakova2, Gennady Sukhikh1, Timur Fatkhudinov2,3 1 Laboratory of Regenerative Medicine, National Medical Research Center for Obstetrics, Gynecology and Perinatology Named after Academician V.I. Kulakov of Ministry of Healthcare of Russian Federation, Moscow, Russia 2 Laboratory of Growth and Development, Scientific Research Institute of Human Morphology, Moscow, Russia 3 Histology Department, Medical Institute, Peoples' Friendship University of Russia, Moscow, Russia 4 Laboratory of Bioinformatic methods for Combinatorial Chemistry and Biology, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences, Moscow, Russia 5 Information Systems Department, Ivannikov Institute for System Programming of the Russian Academy of Sciences, Moscow, Russia 6 Genome Engineering Laboratory, Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region, Russia Figure S1. Flow cytometry analysis of unsorted blood sample. Representative forward, side scattering and histogram are shown. The proportions of negative cells were determined in relation to the isotype controls. The percentages of positive cells are indicated. The blue curve corresponds to the isotype control. Figure S2. Flow cytometry analysis of unsorted liver stromal cells. Representative forward, side scattering and histogram are shown. The proportions of negative cells were determined in relation to the isotype controls. The percentages of positive cells are indicated. The blue curve corresponds to the isotype control. Figure S3. MiRNAs expression analysis in monocytes and Kupffer cells. Full-length of heatmaps are presented.
  • BIOCHEMISTRY of TRYPTOPHAN in HEALTH and DISEASE Contents

    BIOCHEMISTRY of TRYPTOPHAN in HEALTH and DISEASE Contents

    Molec. Aspects Med. Vol. 6, pp. 101-197, 1982 0098-2997/82/020101-97548.50/0 Printed in Great Britain. All rights reserved. Copyright © Pergamon Press Ltd. BIOCHEMISTRY OF TRYPTOPHAN IN HEALTH AND DISEASE David A. Bender Courtauld Institute of Biochemistry, The Middlesex Hospital Medical School, London WIP 7PN, U.K. Contents Chapter 1 THE DISCOVERY OF TRYPTOPHAN, ITS PHYSIOLOGICAL SIGNIFICANCE AND METABOLIC FATES 103 Tryptophan and glucose metabolism 105 Xanthurenic acid and insulin 105 The glucose tolerance factor 106 Inhibition of gluconeogenesis by tryptophan metabolites i07 Metabolic fates of tryptophan 108 Protein synthesis 108 Oxidative metabolism Ii0 5-Hydroxyindole synthesis 111 Intestinal bacterial metabolism iii Chapter 2 THE 5-HYDROXYINDOLE PATHWAY OF TRYPTOPHAN METABOLISM; SEROTONIN AND OTHER CENTRALLY ACTIVE TRYPTOPHAN METABOLITES 112 Tryptophan 5-hydroxylase 112 Inhibition of tryptophan hydroxylase and the carcinoid syndrome 116 Aromatic amino acid decarboxylase 118 The specificity of aromatic amino acid decarboxylase 120 Tryptophan metabolism in the pineal gland 121 Monoamine oxidase 124 The uptake of tryptophan into the brain 124 The binding of tryptophan to serum albumin 127 Competition for uptake by other neutral amino acids 129 Changes in tryptophan metabolism in response to food intake 129 Tryptophan uptake into the brain in liver failure 131 Sleep and tryptophan metabolism 134 101 102 D.A. Bender Tryptophan and serotonin in psychiatric disorders 135 Affective disorders 136 Evidence for a deficit of serotonin or tryptophan
  • 140503 IPF Signatures Supplement Withfigs Thorax

    140503 IPF Signatures Supplement Withfigs Thorax

    Supplementary material for Heterogeneous gene expression signatures correspond to distinct lung pathologies and biomarkers of disease severity in idiopathic pulmonary fibrosis Daryle J. DePianto1*, Sanjay Chandriani1⌘*, Alexander R. Abbas1, Guiquan Jia1, Elsa N. N’Diaye1, Patrick Caplazi1, Steven E. Kauder1, Sabyasachi Biswas1, Satyajit K. Karnik1#, Connie Ha1, Zora Modrusan1, Michael A. Matthay2, Jasleen Kukreja3, Harold R. Collard2, Jackson G. Egen1, Paul J. Wolters2§, and Joseph R. Arron1§ 1Genentech Research and Early Development, South San Francisco, CA 2Department of Medicine, University of California, San Francisco, CA 3Department of Surgery, University of California, San Francisco, CA ⌘Current address: Novartis Institutes for Biomedical Research, Emeryville, CA. #Current address: Gilead Sciences, Foster City, CA. *DJD and SC contributed equally to this manuscript §PJW and JRA co-directed this project Address correspondence to Paul J. Wolters, MD University of California, San Francisco Department of Medicine Box 0111 San Francisco, CA 94143-0111 [email protected] or Joseph R. Arron, MD, PhD Genentech, Inc. MS 231C 1 DNA Way South San Francisco, CA 94080 [email protected] 1 METHODS Human lung tissue samples Tissues were obtained at UCSF from clinical samples from IPF patients at the time of biopsy or lung transplantation. All patients were seen at UCSF and the diagnosis of IPF was established through multidisciplinary review of clinical, radiological, and pathological data according to criteria established by the consensus classification of the American Thoracic Society (ATS) and European Respiratory Society (ERS), Japanese Respiratory Society (JRS), and the Latin American Thoracic Association (ALAT) (ref. 5 in main text). Non-diseased normal lung tissues were procured from lungs not used by the Northern California Transplant Donor Network.